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Objective: Identifying patients at risk for delirium allows prompt application of prevention, diagnostic, and treatment strategies; but is rarely done. Once delirium develops, patients are more likely to need posthospitalization skilled care. This study developed an a priori electronic prediction rule using independent risk factors identified in a National Center of Clinical Excellence meta-analysis and validated the ability to predict delirium in 2 cohorts., Design: Retrospective analysis followed by prospective validation., Setting: Tertiary VA Hospital in New England., Participants: A total of 27,625 medical records of hospitalized patients and 246 prospectively enrolled patients admitted to the hospital., Measurements: The electronic delirium risk prediction rule was created using data obtained from the patient electronic medical record (EMR). The primary outcome, delirium, was identified 2 ways: (1) from the EMR (retrospective cohort) and (2) clinical assessment on enrollment and daily thereafter (prospective participants). We assessed discrimination of the delirium prediction rule with the C-statistic. Secondary outcomes were length of stay and discharge to rehabilitation., Results: Retrospectively, delirium was identified in 8% of medical records (n = 2343); prospectively, delirium during hospitalization was present in 26% of participants (n = 64). In the retrospective cohort, medical record delirium was identified in 2%, 3%, 11%, and 38% of the low, intermediate, high, and very high-risk groups, respectively (C-statistic = 0.81; 95% confidence interval 0.80-0.82). Prospectively, the electronic prediction rule identified delirium in 15%, 18%, 31%, and 55% of these groups (C-statistic = 0.69; 95% confidence interval 0.61-0.77). Compared with low-risk patients, those at high- or very high delirium risk had increased length of stay (5.7 ± 5.6 vs 3.7 ± 2.7 days; P = .001) and higher rates of discharge to rehabilitation (8.9% vs 20.8%; P = .02)., Conclusions: Automatic calculation of delirium risk using an EMR algorithm identifies patients at risk for delirium, which creates a critical opportunity for gaining clinical efficiencies and improving delirium identification, including those needing skilled care., (Published by Elsevier Inc.)